Claude Code Skills · page 23

Follow a structured recovery decision tree when tool calls fail instead of blindly retrying or giving up.

Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.

Periodically self-assess output quality to catch degradation before the judge does.

Author a new Agent Skill for a Hive agent that conforms to the Agent Skills specification (SKILL.md with YAML frontmatter, optional scripts/references/assets directories). Use when the user asks to create, scaffold, add, or package a new skill for a Hive agent.

Required before any hive-browser CLI command. The browser is driven from the terminal by running `hive-browser <command> ... --json` via terminal_exec — not via MCP tools. Teaches the browser lifecycle rules (the bridge attaches to the USER'S running Chrome — never kill or launch browser processes; timeouts are transport issues, not crashes), the screenshot + coordinate workflow (hive-browser interact with a fractional coordinate) that reaches shadow-DOM inputs selectors can't see, the viewport-fraction coordinate rule (not pixels), rich-text editor quirks ("send button stays disabled" failures), and CSP gotchas. Covers Chrome via CDP through the GCU Beeline extension.

Required reading whenever any chart_* tool is available. Teaches the one-tool embedding contract (call chart_render → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no chartjunk, restrained palette, proper typography, single message per chart), and the canonical spec patterns for the 12 most-common chart types. Skipping this leads to 1990s-Excel charts, missing downloads, and the agent writing markdown image links by hand instead of letting chart_render drive the UI.

Read before automating LinkedIn with browser_* tools. LinkedIn combines shadow DOM (#interop-outlet), strict Trusted Types CSP that silently drops innerHTML, Lexical composer, native beforeunload dialogs that hang the bridge, and aggressive spam filters — each has bitten us at least once. Verified flows for profile messaging, connection-request acceptance, feed composition, and search. Requires hive.browser-automation. Verified against logged-in production 2026-04-11.

Required reading whenever any shell_* tool is available. Teaches the foreground/background dichotomy (terminal_exec auto-promotes past 30s, returns a job_id you poll with terminal_job_logs), the standard envelope shape (exit_code, stdout, stdout_truncated_bytes, output_handle, semantic_status, warning, auto_backgrounded, job_id), output handle pagination via terminal_output_get, when to read semantic_status instead of raw exit_code (grep/rg/find/diff/test exit 1 is NOT an error), the destructive-warning surface (rm -rf, git push --force, DROP TABLE), tool preference (the terminal handles ALL file read/write/edit/search; use gcu-tools / hive_tools where they fit), and the bash-only-on-macOS policy. Skipping this leads to "tool returned no output" surprises, orphaned jobs, and panic over benign grep exit codes.

Use terminal_rg / terminal_glob for all filesystem search — your project tree as well as system configs, /var/log, /etc, archive contents. Teaches the rg vs glob vs terminal_exec("find/ls/du/tree") split, common rg flag combos for code/logs/configs, glob patterns for finding files by name, the rule that mtime/size/type predicate queries drop to terminal_exec("find ..."), and that for tree views or single-file stat info you should just use terminal_exec instead of inventing a tool. Read before reaching for raw shell to grep or find anything.

Use when launching anything that runs longer than a minute, anything that streams logs, anything you want to keep running while doing other work — or when terminal_exec auto-backgrounded on you and returned a job_id. Teaches the start→poll→wait pattern with terminal_job_logs offset bookkeeping, the `wait_until_exit=True` blocking-poll idiom, the truncated_bytes_dropped resumption signal, the merge_stderr decision, the SIGINT→SIGTERM→SIGKILL escalation ladder via terminal_job_manage, and the hard rule that jobs die when the terminal-tools server restarts. Read before calling terminal_job_start, or right after terminal_exec auto-backgrounded.

Use when you need state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

Read before automating X / Twitter with browser_* tools. Verified flows for post, reply, delete, search-and-engage, plus the Draft.js compose quirks that silently disable the send button. Includes the daily-reply and job-market-reply playbooks. Requires hive.browser-automation for the underlying screenshot + coordinate workflow. Verified 2026-04-11.

Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF, password-protect a PDF, or render PDF pages as images.

Set up a Slack notification channel (Sentinel) for a colony by driving the browser — reuse or create the "Hive Sentinel" Slack app from a JSON manifest, install it, capture the bot + app tokens, create/select the channel via the Slack API, and turn Sentinel on so the colony can ping the user on Slack and accept replies. Use when the user asks to "set up Slack notifications", "get pinged on Slack", "connect Slack for alerts", "set up Sentinel on Slack", or clicks the in-app "Set this up with the agent" button on the Slack channel step. Requires hive.browser-automation.

Set up a Telegram notification channel (Sentinel) for a colony by driving the browser — create a bot via @BotFather in Telegram Web, store its token, detect the chat to notify, and turn Sentinel on so the colony can ping the user on Telegram and accept replies. Use when the user asks to "set up Telegram notifications", "get pinged on Telegram", "connect Telegram for alerts", "set up Sentinel on Telegram", or clicks the in-app "Set this up with the agent" button on the Telegram step. Requires hive.browser-automation.

Concrete patterns for breaking colony work into parallel worker jobs via run_playbook — when fan-out helps, how to model the goal as a tracker table, write the worker skill, author the playbook, pilot, and let convergence retry/resume the gap.

First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.

Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo.

Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.

Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for save_plan, output_iframe, or slack_attach_html.

Analyze CSV files in the workspace and summarize insights.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure

Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

Technical SEO audit with GEO-specific checks — crawlability, indexability, security, performance, SSR, and AI crawler access

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Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.

Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.

AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.

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AI crawler access analysis. Checks robots.txt, meta tags, and HTTP headers to determine which AI crawlers can access the site. Provides a complete access map and recommendations for maximizing AI visibility while maintaining appropriate control.

Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.

Platform-specific AI search optimization — audit and optimize for Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot individually

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Generate a professional PDF report from a GEO audit using pandoc + Chrome headless. Converts GEO-AUDIT-REPORT.md into a styled, client-ready PDF with a cover page, color-coded score tables, severity-tagged findings, and a 90-day roadmap.

Generate a professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions

Pull the latest GEO-SEO skill updates from the upstream repository. Compares installed files against the latest release, shows what changed, and updates all skills, agents, scripts, and schema templates in place.

Clone the current conversation so the user can branch off and try a different approach.

Analyze GitHub Actions failures and identify root causes

Clone the later half of the current conversation, discarding earlier context to reduce token usage while preserving recent work.

Write or update a handoff document so the next agent with fresh context can continue this work.

Fetch content from Reddit via its JSON API using a browser session (DuckDuckGo-hop unlock). Use when accessing Reddit URLs, researching topics on Reddit, or when Reddit returns 403/blocked errors.

Review recent conversations to find improvements for CLAUDE.md files.

Fetch and summarize Hacker News / hckrnews.com top stories, articles, and their comment threads. Use when asked to summarize HN front-page stories, a specific HN story plus its discussion, or "the top N from hckrnews".

Recommend which Claude Code version to run, or whether to update. Use when asked which Claude Code version is best/safe, whether to update now, whether a recent release is buggy, or what changed since the installed version.

Full-text search across all of the user's GitHub repos (including private ones) using a local mirror and ripgrep. Use for "where did I put X", "which repo has X", or any search spanning the user's repos - gh search code / the REST API cannot reliably search private repos.

Clone the last quarter of the current conversation, discarding earlier context to reduce token usage while preserving recent work.

Comprehensive backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware, or working with Express APIs, Prisma database access, Sentry error tracking, Zod validation, unifiedConfig, dependency injection, or async patterns. Covers layered architecture (routes → controllers → services → repositories), BaseController pattern, error handling, performance monitoring, testing strategies, and migration from legacy patterns.

Add Sentry v8 error tracking and performance monitoring to your project services. Use this skill when adding error handling, creating new controllers, instrumenting cron jobs, or tracking database performance. ALL ERRORS MUST BE CAPTURED TO SENTRY - no exceptions.

Frontend development guidelines for React/TypeScript applications. Modern patterns including Suspense, lazy loading, useSuspenseQuery, file organization with features directory, MUI v7 styling, TanStack Router, performance optimization, and TypeScript best practices. Use when creating components, pages, features, fetching data, styling, routing, or working with frontend code.

Test authenticated routes in the your project using cookie-based authentication. Use this skill when testing API endpoints, validating route functionality, or debugging authentication issues. Includes patterns for using test-auth-route.js and mock authentication.

Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.

Spin up a live local Omnigent server and exercise the Antigravity (Gemini) SDK harness end-to-end — build antigravity agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity harness (omnigent/inner/antigravity_executor.py, antigravity_harness.py, omnigent/onboarding/antigravity_auth.py) or its auth / model / tool-bridge behavior.

Spin up a live local Omnigent server and exercise the Cursor SDK harness end-to-end — build cursor agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the cursor harness (omnigent/inner/cursor_executor.py, cursor_harness.py, cursor_auth.py) or its auth / model / tool-bridge behavior.

Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack for a new platform.

Have the Claude and GPT partners critique each other's answers across a configurable number of rounds (default 1) before converging on a synthesis. Use when the user wants the two perspectives stress-tested against each other, not just shown side by side.

Verify an implementer's diff with an INDEPENDENT, different-vendor sub-agent (diff plus contract only); turn blocking issues into fix-tasks and loop until clean.

Run independent subtasks in parallel — one git worktree and one implementation sub-agent per task, each opening its own PR — then cross-review every PR. polly never merges; the human does.

Delegate read-only investigation, debugging, audit, search, or code-understanding tasks to sub-agents; synthesize only from their structured reports.

Patterns and templates for generating valid Omnigent agent directories. Load when ready to create files.

Detect Python agent frameworks from code imports and map them to Omnigent executor types. Load when the user has existing agent code to integrate.

Deep reference on Omnigent config format, executor types, skill/tool structure, and conventions. Load when you need to look up how the platform works.

Test counter — codex skills e2e. The unique suffix b8d4e7 appears in the skill name so a string match in the agent's output proves codex loaded this skill.

Test greeter — codex skills e2e. The unique suffix a3f9c2 appears in the skill name so a string match in the agent's output proves codex loaded this skill.

Test counter — pi skills e2e. The unique suffix d2f6e1 appears in the skill name so a string match in the agent's output proves Pi loaded this skill.

Test greeter — pi skills e2e. The unique suffix c4a8d5 appears in the skill name so a string match in the agent's output proves Pi loaded this skill.

Research checklist and source-quality guidance for the archer e2e fixture.

Spin up a live local Omnigent server and exercise the GitHub Copilot SDK harness end-to-end — build copilot agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the copilot harness (omnigent/inner/copilot_executor.py, copilot_harness.py, omnigent/onboarding/copilot_auth.py) or its auth / model / tool-bridge behavior.

Document a module or public API surface (functions, classes, CLI commands, endpoints) from the code itself. Use when the user asks for API reference, to document a module, or to write usage docs for a public interface.

Turn a breaking change (an API rename, removed flag, changed default, or moved module) into concrete upgrade steps with before/after examples. Use when the user asks how to migrate, upgrade, or adapt to a breaking change.

Verify the Omnigent CLI's setup/onboarding flow, terminal UI/UX, and critical user journeys in a completely isolated, reproducible loop. Drives the real `omnigent` binary through a PTY (pexpect) inside a throwaway OMNIGENT_CONFIG_HOME / OMNIGENT_DATA_DIR sandbox that never touches the user's real ~/.omnigent, captures ANSI-stripped frames for UX inspection, and proves a change is verifiable via a before→fix→after baseline diff. Load when developing or reviewing a CLI setup/onboarding/REPL/picker change (omnigent/cli.py, omnigent/onboarding/*, omnigent/repl/*, scripts/install_oss.sh), reproducing a cold-start/first-run UX bug, or confirming a fix actually lands. Several agents can run it concurrently on separate worktrees.

Spin up a live local Omnigent server + runner and exercise the native Antigravity (agy) TUI harness (antigravity-native) end-to-end — launch the real `agy` CLI via `omnigent antigravity`, drive turns through the web UI, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity-native harness (omnigent/inner/antigravity_native_executor.py, omnigent/antigravity_native.py, antigravity_native_bridge.py, antigravity_native_rpc.py, antigravity_native_reader.py, antigravity_native_launch.py) or its agy launch / RPC mirror / tmux delivery / OAuth / MCP-relay behavior. NOT the in-process `antigravity` Gemini SDK harness.

Reference guide for building new Omnigent harness integrations — covers SDK/subprocess harnesses and native harnesses as separate tracks, each with their own feature matrix, implementation patterns, and prioritized checklist.

Spin up a live local Omnigent server + runner and exercise the native Pi TUI harness (pi-native) end-to-end — launch the real `pi` CLI via `omnigent pi`, drive turns through the web/bridge, smoke-test, and bug-bash. Load when developing, testing, or debugging the pi-native harness (omnigent/inner/pi_native_executor.py, pi_native_harness.py, omnigent/pi_native.py, pi_native_bridge.py, pi_native_credentials.py) or its bridge / extension / auth / model behavior.

End-to-end test the polly multi-agent coding orchestrator's critical user journeys (CUJs). Two halves — a deterministic mock-LLM driver (polly_cuj.py) that boots a throwaway local server + mock LLM and asserts the substrate (boot, bridged sys_* tool dispatch, the blast_radius / spawn_bounds / headless_subagent_purpose_guard guardrails, fan-out delegation), and a live real-CLI recipe (real claude/codex/pi, real worktrees/PRs) for polly's actual judgment. Load when developing, testing, or debugging examples/polly — its config.yaml, the claude_code/codex/pi sub-agents, the investigate/fanout/cross-review skills, or the omnigent.inner.nessie.policies guardrails — or reproducing a polly orchestration bug.

Audit a codebase or directory for security issues (hardcoded secrets, injection, unsafe deserialization, weak crypto, authz gaps) and produce a structured findings report. Use when the user asks for a security review, an audit, or to check code for vulnerabilities. Report only — never fix.

当任务需要写代码实际获取A股数据时使用——拉取行情/K线(mootdx+腾讯+百度)、研报(东财+同花顺+iwencai)、信号(热点/北向/龙虎榜/解禁/行业)、资金面(融资融券/大宗/股东户数/分红/资金流)、新闻、财务三表/F10、公告(巨潮)、打板(涨停池/连板/炸板率/重点监控池/日内异动)、ETF期权(T型报价/希腊字母/IV)、舆情互动(互动易/热榜/人气榜)、筹码分布(获利比例/成本区间)、复权因子、估值历史(PE/PB/PS+换手率+ST)、申万行业变迁史、宏观(社融/PMI)等真实数据。十一层数据源·54端点(含3官方备胎)·内嵌全部可运行代码，自包含零依赖外部文件；优先用通达信(mootdx)/腾讯(不封IP)，东财接口已内置限流防封，主源被封可查「备用源速查」降级。仅在需要调用数据接口取数时使用：A股概念解释、投资观点讨论、策略问答等无需取数的话题不要加载本skill。

Create, edit, synchronize, inspect, test, and publish editable draw.io diagrams. Use when the user explicitly requests draw.io/diagrams.net, needs a polished architecture, ERD, UML, sequence, C4, SysML, BPMN, network, swimlane, ML, or infrastructure diagram, wants code/IaC/SQL/OpenAPI converted into a diagram, or wants an existing diagram queried, reviewed, diffed, restyled, kept in sync, or made interactive. Prefer Mermaid/PlantUML elsewhere when the requested artifact is diagrams-as-code rather than an editable draw.io file.

Operate professional paid advertising across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for account intake, source-grounded audits, strategy, budget and measurement planning, creative production, experiments, reporting, monitoring, and explicitly approved campaign changes. Also trigger on PPC, paid social, retail media, attribution, tracking, landing pages, cross-platform conversion totals, negative keywords or search terms, beta-feature scoring, stale platform claims, API-token or credential setup, campaign deletion, and safe Claude Ads installation or uninstall.

Audit Amazon Ads profiles, regions, Sponsored Products, Sponsored Brands, Sponsored Display, DSP, portfolios, targeting, search terms, retail readiness, creative, budgets, ACOS, TACOS, reporting, and policy. Use for Amazon Ads, sponsored ads, Amazon PPC, ACOS, TACOS, ASIN advertising, Amazon DSP, or retail-media optimization.

Audit Apple Ads measurement, AdServices and AdAttributionKit, campaign and keyword structure, Search Match, App Store placements, custom product pages, bidding, budgets, MMP reconciliation, and policy. Use for Apple Ads, Apple Search Ads, App Store ads, Search Match, custom product pages, AdServices, or Apple app-install campaigns.

Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation. Use for attribution audit, attribution models, conversion windows, requests to add or total Meta and Google conversions, incompatible reporting-window aggregation, GA4 attribution, MMP review, AppsFlyer, Adjust, Branch, Singular, or cross-platform discrepancies.

Run a source-grounded paid-advertising audit for one or more of Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform weighting, beta-feature eligibility and scoring, spend audits, tracking audits, or prioritized opportunities and risks.

Plan and review paid-media budgets, bidding, pacing, marginal return, forecasts, CPA, ROAS, MER, LTV:CAC, constraints, and allocation across supported platforms. Use for ad budget allocation, media budget, bidding strategy, scaling, spend pacing, budget forecast, ROAS target, or investment tradeoffs.

Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.

Create source-grounded paid-ad campaign concepts, messaging, copy, creative briefs, and production plans from a validated brand profile, campaign objective, platform requirements, and optional audit evidence. Triggers on: campaign brief, campaign concepts, create a campaign, ad concepts, ad copy, ad messaging, creative brief, headlines, descriptions.

Audit paid-ad copy, images, video, hooks, concepts, format coverage, platform-native fit, message match, creative fatigue, accessibility, and policy across supported platforms. Use for creative audit, ad creative, creative fatigue, creative diversity, ad copy review, video review, image review, or production priorities.

Extract a public-safe brand and offer profile for paid advertising from an authorized website and operator input. Triggers on: brand DNA, brand profile, brand identity, brand style, brand colors, brand voice, visual identity, style guide, website brand analysis.

Generate paid-ad image assets from a validated creative brief and brand profile using an explicitly configured image provider. Triggers on: generate ads, generate ad images, create ad creatives, create ad images, make ad images, generate visuals, make campaign visuals, generate images from campaign brief.

Audit Google Ads measurement, Search, Shopping, Performance Max, Demand Gen, YouTube-linked inventory, keywords and search terms, negative-keyword generation or review, creative assets, bidding, budgets, settings, and policy. Use for Google Ads, AdWords, Search campaigns, search terms reports, broad negatives, Shopping, Performance Max, PMax, Demand Gen, GAQL, Google conversion tracking, or Google campaign optimization.

Audit paid-ad landing pages for message match, mobile experience, performance, accessibility, trust, forms, consent, tracking, security, and conversion friction. Use for landing-page audit, post-click experience, LP audit, conversion-rate optimization, form optimization, ad-to-page message match, redirects, blocked navigation, or requests involving private, loopback, link-local, or metadata IP destinations.

Draft or explicitly apply a paid-ad campaign launch through Claude Ads capability-gated adapters. Use for campaign creation, launch plans, publishing ads, activating campaigns, uploading creative, or requests to push a campaign live.

Audit LinkedIn Ads measurement, Insight Tag and conversions, professional audiences, lead generation, ABM, creative, bidding, budgets, pacing, automation, and policy. Use for LinkedIn Ads, Campaign Manager, Insight Tag, Lead Gen Forms, Thought Leader Ads, ABM campaigns, or B2B paid media.

Calculate and model paid-media CPA, CPL, CPC, CPM, ROAS, MER, break-even targets, contribution margin, LTV:CAC, impression-share opportunity, budgets, forecasts, and experiment economics. Use for PPC math, ad calculator, break-even analysis, ROAS calculator, CPA calculator, budget forecast, LTV CAC, or MER.

Audit Meta Ads measurement, Pixel and Conversions API, attribution, Facebook and Instagram creative, audiences, placements, automation, budgets, account structure, and policy. Use for Meta Ads, Facebook Ads, Instagram Ads, Advantage+, Pixel, CAPI, Events Manager, creative fatigue, or Meta campaign optimization.